[Colloquium] REMINDER: 4/13 Talks at TTIC: William Wang, CMU

[Mary Marre]

*When:  *   Wednesday, April 13th at 11:00 am

*Where:*    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

*Who: *      William Wang, CMU


*Title: *      Scalable Learning and Reasoning for Large Knowledge Graphs

*Abstract:*

Learning to reason and understand the world’s knowledge is a fundamental
problem in Artificial Intelligence (AI). While it is always hypothesized
that both the symbolic and statistical approaches are necessary to tackle
complex problems in AI, in practice, bridging the two in a combined
framework might bring intractability—most probabilistic first-order logics
are simply not efficient enough for real-world sized tasks.

With the vast amount of relational data available in digital form, now is a
good opportunity to close the gap between these two paradigms. The core
research question that I will address in this talk is the following: how
can we design scalable statistical learning and inference methods to
operate over rich knowledge representations? In this talk, I will describe
some examples of my work in advancing the state-of-the-arts in theories and
practices of statistical relational learning, including: 1) ProPPR, a
scalable learning and reasoning framework whose inference time does not
depend on the size of knowledge graphs; 2) an efficient structural gradient
based meta-reasoning approach that learns formulas from relational data; 3)
and an application of joint information extraction and relational reasoning
in NLP. I will conclude this talk by describing my other research interests
and my future research plans in the interdisciplinary field of data science.


*Bio: *

William Wang is a final-year PhD student at the School of Computer Science,
Carnegie Mellon University. He has broad interests in machine learning
approaches to data science, including statistical relational learning,
information extraction, social media, speech, and vision. He has published
more than 30 papers at leading conferences and journals including ACL,
EMNLP, NAACL, IJCAI, CIKM, ICASSP, ASRU, Machine Learning, and Computer
Speech & Language. He has received best paper awards (or nominations) at
ASRU, CIKM, and EMNLP, a best reviewer award at NAACL 2015, the Richard
King Mellon Presidential Fellowship in 2011, and he is a Facebook
Fellowship finalist. He is an alumnus of Columbia University, and a former
research scientist intern at Yahoo! Labs, Microsoft Research Redmond, and
University of Southern California. In addition to research, William enjoys
writing scientific articles that impact the broader online community: his
microblog has more than 2,000,000 views each month. More:
https://www.cs.cmu.edu/~yww/

Host: Matthew Walter, mwalter at ttic.edu





Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*
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